Overview

Dataset statistics

Number of variables30
Number of observations33147
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory240.0 B

Variable types

Categorical7
Numeric23

Warnings

name has a high cardinality: 33114 distinct values High cardinality
venture is highly correlated with round_B and 6 other fieldsHigh correlation
round_B is highly correlated with venture and 1 other fieldsHigh correlation
round_C is highly correlated with venture and 1 other fieldsHigh correlation
round_D is highly correlated with venture and 1 other fieldsHigh correlation
round_E is highly correlated with venture and 1 other fieldsHigh correlation
round_G is highly correlated with round_HHigh correlation
round_H is highly correlated with round_GHigh correlation
total_disclosed_venture_funding is highly correlated with venture and 6 other fieldsHigh correlation
Cluster_NO_PCA is highly correlated with venture and 4 other fieldsHigh correlation
kmean_NO_PCA is highly correlated with venture and 4 other fieldsHigh correlation
ClusterWITH_PCA is highly correlated with Cluster_NO_PCA and 2 other fieldsHigh correlation
kmean_WITH_PCA is highly correlated with Cluster_NO_PCA and 2 other fieldsHigh correlation
funding_rounds is highly correlated with venture and 1 other fieldsHigh correlation
venture is highly correlated with funding_rounds and 3 other fieldsHigh correlation
round_A is highly correlated with venture and 1 other fieldsHigh correlation
round_B is highly correlated with venture and 1 other fieldsHigh correlation
round_C is highly correlated with Cluster_NO_PCA and 1 other fieldsHigh correlation
total_disclosed_venture_funding is highly correlated with funding_rounds and 3 other fieldsHigh correlation
Cluster_NO_PCA is highly correlated with round_C and 3 other fieldsHigh correlation
kmean_NO_PCA is highly correlated with round_C and 3 other fieldsHigh correlation
ClusterWITH_PCA is highly correlated with Cluster_NO_PCA and 2 other fieldsHigh correlation
kmean_WITH_PCA is highly correlated with Cluster_NO_PCA and 2 other fieldsHigh correlation
market is highly correlated with status and 23 other fieldsHigh correlation
status is highly correlated with market and 23 other fieldsHigh correlation
funding_rounds is highly correlated with equity_crowdfunding and 21 other fieldsHigh correlation
seed is highly correlated with equity_crowdfunding and 22 other fieldsHigh correlation
venture is highly correlated with equity_crowdfunding and 22 other fieldsHigh correlation
equity_crowdfunding is highly correlated with market and 26 other fieldsHigh correlation
undisclosed is highly correlated with market and 26 other fieldsHigh correlation
convertible_note is highly correlated with market and 26 other fieldsHigh correlation
debt_financing is highly correlated with market and 25 other fieldsHigh correlation
angel is highly correlated with market and 26 other fieldsHigh correlation
grant is highly correlated with market and 25 other fieldsHigh correlation
private_equity is highly correlated with market and 26 other fieldsHigh correlation
post_ipo_equity is highly correlated with market and 25 other fieldsHigh correlation
post_ipo_debt is highly correlated with market and 26 other fieldsHigh correlation
secondary_market is highly correlated with market and 25 other fieldsHigh correlation
product_crowdfunding is highly correlated with market and 26 other fieldsHigh correlation
round_A is highly correlated with market and 19 other fieldsHigh correlation
round_B is highly correlated with market and 26 other fieldsHigh correlation
round_C is highly correlated with market and 26 other fieldsHigh correlation
round_D is highly correlated with market and 26 other fieldsHigh correlation
round_E is highly correlated with market and 26 other fieldsHigh correlation
round_F is highly correlated with market and 26 other fieldsHigh correlation
round_G is highly correlated with market and 26 other fieldsHigh correlation
round_H is highly correlated with market and 26 other fieldsHigh correlation
total_disclosed_venture_funding is highly correlated with venture and 5 other fieldsHigh correlation
Cluster_NO_PCA is highly correlated with market and 27 other fieldsHigh correlation
kmean_NO_PCA is highly correlated with market and 27 other fieldsHigh correlation
ClusterWITH_PCA is highly correlated with market and 27 other fieldsHigh correlation
kmean_WITH_PCA is highly correlated with market and 27 other fieldsHigh correlation
venture is highly correlated with round_D and 11 other fieldsHigh correlation
round_D is highly correlated with venture and 6 other fieldsHigh correlation
round_G is highly correlated with venture and 8 other fieldsHigh correlation
kmean_NO_PCA is highly correlated with venture and 10 other fieldsHigh correlation
round_H is highly correlated with venture and 3 other fieldsHigh correlation
post_ipo_debt is highly correlated with post_ipo_equityHigh correlation
seed is highly correlated with product_crowdfundingHigh correlation
round_E is highly correlated with venture and 8 other fieldsHigh correlation
total_disclosed_venture_funding is highly correlated with venture and 11 other fieldsHigh correlation
secondary_market is highly correlated with round_GHigh correlation
ClusterWITH_PCA is highly correlated with venture and 10 other fieldsHigh correlation
product_crowdfunding is highly correlated with seedHigh correlation
round_B is highly correlated with venture and 6 other fieldsHigh correlation
round_F is highly correlated with venture and 6 other fieldsHigh correlation
round_C is highly correlated with venture and 7 other fieldsHigh correlation
Cluster_NO_PCA is highly correlated with venture and 10 other fieldsHigh correlation
kmean_WITH_PCA is highly correlated with venture and 10 other fieldsHigh correlation
post_ipo_equity is highly correlated with post_ipo_debtHigh correlation
Cluster_NO_PCA is highly correlated with ClusterWITH_PCA and 2 other fieldsHigh correlation
ClusterWITH_PCA is highly correlated with Cluster_NO_PCA and 2 other fieldsHigh correlation
kmean_NO_PCA is highly correlated with Cluster_NO_PCA and 2 other fieldsHigh correlation
kmean_WITH_PCA is highly correlated with Cluster_NO_PCA and 2 other fieldsHigh correlation
seed is highly skewed (γ1 = 41.15012631) Skewed
venture is highly skewed (γ1 = 22.85301651) Skewed
equity_crowdfunding is highly skewed (γ1 = 60.98627495) Skewed
undisclosed is highly skewed (γ1 = 63.09075926) Skewed
convertible_note is highly skewed (γ1 = 169.7140714) Skewed
debt_financing is highly skewed (γ1 = 175.879695) Skewed
angel is highly skewed (γ1 = 23.44258673) Skewed
grant is highly skewed (γ1 = 69.77439829) Skewed
private_equity is highly skewed (γ1 = 49.38797699) Skewed
post_ipo_equity is highly skewed (γ1 = 122.7492992) Skewed
post_ipo_debt is highly skewed (γ1 = 102.1669249) Skewed
secondary_market is highly skewed (γ1 = 109.3906662) Skewed
product_crowdfunding is highly skewed (γ1 = 116.33056) Skewed
round_D is highly skewed (γ1 = 54.86374714) Skewed
round_E is highly skewed (γ1 = 28.35215798) Skewed
round_F is highly skewed (γ1 = 90.23753602) Skewed
round_G is highly skewed (γ1 = 128.2336611) Skewed
total_disclosed_venture_funding is highly skewed (γ1 = 25.33427115) Skewed
name is uniformly distributed Uniform
seed has 23172 (69.9%) zeros Zeros
venture has 16673 (50.3%) zeros Zeros
equity_crowdfunding has 32718 (98.7%) zeros Zeros
undisclosed has 32643 (98.5%) zeros Zeros
convertible_note has 32707 (98.7%) zeros Zeros
debt_financing has 29971 (90.4%) zeros Zeros
angel has 30654 (92.5%) zeros Zeros
grant has 32413 (97.8%) zeros Zeros
private_equity has 32135 (96.9%) zeros Zeros
post_ipo_equity has 32982 (99.5%) zeros Zeros
post_ipo_debt has 33104 (99.9%) zeros Zeros
secondary_market has 33132 (> 99.9%) zeros Zeros
product_crowdfunding has 32999 (99.6%) zeros Zeros
round_A has 26195 (79.0%) zeros Zeros
round_B has 28688 (86.5%) zeros Zeros
round_C has 30727 (92.7%) zeros Zeros
round_D has 32024 (96.6%) zeros Zeros
round_E has 32686 (98.6%) zeros Zeros
round_F has 32991 (99.5%) zeros Zeros
round_G has 33115 (99.9%) zeros Zeros
total_disclosed_venture_funding has 7657 (23.1%) zeros Zeros

Reproduction

Analysis started2021-09-04 03:54:34.094034
Analysis finished2021-09-04 04:04:35.194559
Duration10 minutes and 1.1 second
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct33114
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size259.1 KiB
Roost
 
3
Spire
 
3
Breeze
 
2
Cue
 
2
inMarket
 
2
Other values (33109)
33135 

Length

Max length59
Median length10
Mean length11.87823936
Min length1

Characters and Unicode

Total characters393728
Distinct characters131
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33083 ?
Unique (%)99.8%

Sample

1st row#waywire
2nd row'Rock' Your Paper
3rd row(In)Touch Network
4th row-R- Ranch and Mine
5th row004 Technologies

Common Values

ValueCountFrequency (%)
Roost3
 
< 0.1%
Spire3
 
< 0.1%
Breeze2
 
< 0.1%
Cue2
 
< 0.1%
inMarket2
 
< 0.1%
Hubbub2
 
< 0.1%
Stitch2
 
< 0.1%
trippiece2
 
< 0.1%
Fastpoint Games2
 
< 0.1%
Tiempo2
 
< 0.1%
Other values (33104)33125
99.9%

Length

2021-09-04T12:04:35.703761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
technologies800
 
1.5%
systems498
 
1.0%
inc498
 
1.0%
solutions411
 
0.8%
media397
 
0.8%
group322
 
0.6%
medical311
 
0.6%
software303
 
0.6%
the291
 
0.6%
networks289
 
0.6%
Other values (31914)47773
92.1%

Most occurring characters

ValueCountFrequency (%)
e37516
 
9.5%
o27543
 
7.0%
i26927
 
6.8%
a26322
 
6.7%
n21384
 
5.4%
t21282
 
5.4%
r21055
 
5.3%
18745
 
4.8%
s16743
 
4.3%
l15567
 
4.0%
Other values (121)160644
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter301332
76.5%
Uppercase Letter68900
 
17.5%
Space Separator18745
 
4.8%
Other Punctuation2472
 
0.6%
Decimal Number1463
 
0.4%
Dash Punctuation384
 
0.1%
Open Punctuation172
 
< 0.1%
Close Punctuation172
 
< 0.1%
Math Symbol31
 
< 0.1%
Control29
 
< 0.1%
Other values (7)28
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e37516
12.5%
o27543
 
9.1%
i26927
 
8.9%
a26322
 
8.7%
n21384
 
7.1%
t21282
 
7.1%
r21055
 
7.0%
s16743
 
5.6%
l15567
 
5.2%
c13310
 
4.4%
Other values (33)73683
24.5%
Uppercase Letter
ValueCountFrequency (%)
S7822
 
11.4%
C5560
 
8.1%
T5157
 
7.5%
M4706
 
6.8%
A4402
 
6.4%
P4215
 
6.1%
I3541
 
5.1%
B3286
 
4.8%
L2911
 
4.2%
E2863
 
4.2%
Other values (21)24437
35.5%
Other Punctuation
ValueCountFrequency (%)
.1740
70.4%
,331
 
13.4%
&167
 
6.8%
'93
 
3.8%
!46
 
1.9%
?33
 
1.3%
/32
 
1.3%
:12
 
0.5%
@8
 
0.3%
*5
 
0.2%
Other values (4)5
 
0.2%
Decimal Number
ValueCountFrequency (%)
2294
20.1%
3214
14.6%
1210
14.4%
0187
12.8%
4155
10.6%
696
 
6.6%
593
 
6.4%
881
 
5.5%
974
 
5.1%
759
 
4.0%
Control
ValueCountFrequency (%)
™10
34.5%
’10
34.5%
„2
 
6.9%
€2
 
6.9%
‹1
 
3.4%
…1
 
3.4%
‘1
 
3.4%
•1
 
3.4%
–1
 
3.4%
Math Symbol
ValueCountFrequency (%)
+16
51.6%
|13
41.9%
±1
 
3.2%
~1
 
3.2%
Other Number
ValueCountFrequency (%)
²4
57.1%
¾1
 
14.3%
½1
 
14.3%
¼1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
(168
97.7%
[3
 
1.7%
{1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
)168
97.7%
]3
 
1.7%
}1
 
0.6%
Other Symbol
ValueCountFrequency (%)
©5
38.5%
®5
38.5%
°3
23.1%
Space Separator
ValueCountFrequency (%)
18745
100.0%
Dash Punctuation
ValueCountFrequency (%)
-384
100.0%
Format
ValueCountFrequency (%)
­1
100.0%
Modifier Symbol
ValueCountFrequency (%)
^1
100.0%
Currency Symbol
ValueCountFrequency (%)
¢3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%
Other Letter
ValueCountFrequency (%)
º1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin370232
94.0%
Common23496
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e37516
 
10.1%
o27543
 
7.4%
i26927
 
7.3%
a26322
 
7.1%
n21384
 
5.8%
t21282
 
5.7%
r21055
 
5.7%
s16743
 
4.5%
l15567
 
4.2%
c13310
 
3.6%
Other values (64)142583
38.5%
Common
ValueCountFrequency (%)
18745
79.8%
.1740
 
7.4%
-384
 
1.6%
,331
 
1.4%
2294
 
1.3%
3214
 
0.9%
1210
 
0.9%
0187
 
0.8%
(168
 
0.7%
)168
 
0.7%
Other values (47)1055
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII393600
> 99.9%
Latin 1 Sup128
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e37516
 
9.5%
o27543
 
7.0%
i26927
 
6.8%
a26322
 
6.7%
n21384
 
5.4%
t21282
 
5.4%
r21055
 
5.3%
18745
 
4.8%
s16743
 
4.3%
l15567
 
4.0%
Other values (78)160516
40.8%
Latin 1 Sup
ValueCountFrequency (%)
é17
 
13.3%
™10
 
7.8%
’10
 
7.8%
Ã7
 
5.5%
á6
 
4.7%
ó5
 
3.9%
ö5
 
3.9%
©5
 
3.9%
®5
 
3.9%
â4
 
3.1%
Other values (33)54
42.2%

market
Real number (ℝ≥0)

HIGH CORRELATION

Distinct720
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.78634567
Minimum0
Maximum719
Zeros271
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:35.868343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q114
median33
Q3103
95-th percentile380.7
Maximum719
Range719
Interquartile range (IQR)89

Descriptive statistics

Standard deviation125.0020075
Coefficient of variation (CV)1.423934514
Kurtosis5.084460271
Mean87.78634567
Median Absolute Deviation (MAD)29
Skewness2.257136586
Sum2909854
Variance15625.50187
MonotonicityNot monotonic
2021-09-04T12:04:36.015047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43524
 
10.6%
172361
 
7.1%
201425
 
4.3%
51262
 
3.8%
101163
 
3.5%
36997
 
3.0%
87819
 
2.5%
26814
 
2.5%
24803
 
2.4%
62770
 
2.3%
Other values (710)19209
58.0%
ValueCountFrequency (%)
0271
 
0.8%
156
 
0.2%
227
 
0.1%
314
 
< 0.1%
43524
10.6%
51262
 
3.8%
6621
 
1.9%
7319
 
1.0%
8600
 
1.8%
9212
 
0.6%
ValueCountFrequency (%)
7191
< 0.1%
7181
< 0.1%
7171
< 0.1%
7161
< 0.1%
7151
< 0.1%
7141
< 0.1%
7131
< 0.1%
7121
< 0.1%
7111
< 0.1%
7101
< 0.1%

status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size259.1 KiB
1
28667 
0
 
2763
2
 
1717

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33147
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
128667
86.5%
02763
 
8.3%
21717
 
5.2%

Length

2021-09-04T12:04:36.292884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-04T12:04:36.371489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
128667
86.5%
02763
 
8.3%
21717
 
5.2%

Most occurring characters

ValueCountFrequency (%)
128667
86.5%
02763
 
8.3%
21717
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33147
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
128667
86.5%
02763
 
8.3%
21717
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common33147
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
128667
86.5%
02763
 
8.3%
21717
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII33147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
128667
86.5%
02763
 
8.3%
21717
 
5.2%

funding_rounds
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.869399946
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:36.453436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum18
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.433280888
Coefficient of variation (CV)0.7667063922
Kurtosis9.574585632
Mean1.869399946
Median Absolute Deviation (MAD)0
Skewness2.575518933
Sum61965
Variance2.054294104
MonotonicityNot monotonic
2021-09-04T12:04:36.569454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
119214
58.0%
26946
 
21.0%
33324
 
10.0%
41725
 
5.2%
5890
 
2.7%
6512
 
1.5%
7226
 
0.7%
8133
 
0.4%
975
 
0.2%
1041
 
0.1%
Other values (7)61
 
0.2%
ValueCountFrequency (%)
119214
58.0%
26946
 
21.0%
33324
 
10.0%
41725
 
5.2%
5890
 
2.7%
6512
 
1.5%
7226
 
0.7%
8133
 
0.4%
975
 
0.2%
1041
 
0.1%
ValueCountFrequency (%)
181
 
< 0.1%
161
 
< 0.1%
154
 
< 0.1%
143
 
< 0.1%
137
 
< 0.1%
1212
 
< 0.1%
1133
 
0.1%
1041
 
0.1%
975
0.2%
8133
0.4%

seed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2596
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259134.8011
Minimum0
Maximum100000000
Zeros23172
Zeros (%)69.9%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:36.696042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q350000
95-th percentile1600000
Maximum100000000
Range100000000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation997840.1687
Coefficient of variation (CV)3.8506606
Kurtosis3552.393534
Mean259134.8011
Median Absolute Deviation (MAD)0
Skewness41.15012631
Sum8589541253
Variance9.956850023 × 1011
MonotonicityNot monotonic
2021-09-04T12:04:36.848370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023172
69.9%
1000000447
 
1.3%
500000395
 
1.2%
100000373
 
1.1%
40000364
 
1.1%
2000000253
 
0.8%
250000252
 
0.8%
50000251
 
0.8%
1500000219
 
0.7%
200000207
 
0.6%
Other values (2586)7214
 
21.8%
ValueCountFrequency (%)
023172
69.9%
601
 
< 0.1%
100017
 
0.1%
13331
 
< 0.1%
15001
 
< 0.1%
15061
 
< 0.1%
20005
 
< 0.1%
24141
 
< 0.1%
24541
 
< 0.1%
25003
 
< 0.1%
ValueCountFrequency (%)
1000000001
< 0.1%
640000001
< 0.1%
250000001
< 0.1%
248331771
< 0.1%
210000001
< 0.1%
150000001
< 0.1%
140813471
< 0.1%
115000002
< 0.1%
104793841
< 0.1%
101200001
< 0.1%

venture
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct6921
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9044224.184
Minimum0
Maximum2351000000
Zeros16673
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:36.983859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36133073
95-th percentile44385780
Maximum2351000000
Range2351000000
Interquartile range (IQR)6133073

Descriptive statistics

Standard deviation33060345.06
Coefficient of variation (CV)3.655409727
Kurtosis1087.450603
Mean9044224.184
Median Absolute Deviation (MAD)0
Skewness22.85301651
Sum2.99788899 × 1011
Variance1.092986415 × 1015
MonotonicityNot monotonic
2021-09-04T12:04:37.128861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016673
50.3%
5000000333
 
1.0%
10000000327
 
1.0%
2000000290
 
0.9%
1000000270
 
0.8%
3000000253
 
0.8%
4000000228
 
0.7%
6000000211
 
0.6%
15000000171
 
0.5%
1500000165
 
0.5%
Other values (6911)14226
42.9%
ValueCountFrequency (%)
016673
50.3%
2911
 
< 0.1%
7151
 
< 0.1%
12651
 
< 0.1%
13051
 
< 0.1%
15002
 
< 0.1%
15521
 
< 0.1%
20002
 
< 0.1%
22501
 
< 0.1%
25001
 
< 0.1%
ValueCountFrequency (%)
23510000001
< 0.1%
15060000001
< 0.1%
12010000001
< 0.1%
11360000001
< 0.1%
8665507861
< 0.1%
8182250391
< 0.1%
7942000001
< 0.1%
7750000001
< 0.1%
7620000001
< 0.1%
7601665111
< 0.1%

equity_crowdfunding
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct202
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6463.938939
Minimum0
Maximum17000000
Zeros32718
Zeros (%)98.7%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:37.270595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum17000000
Range17000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation180253.2074
Coefficient of variation (CV)27.88597001
Kurtosis4759.765502
Mean6463.938939
Median Absolute Deviation (MAD)0
Skewness60.98627495
Sum214260184
Variance3.249121877 × 1010
MonotonicityNot monotonic
2021-09-04T12:04:37.423778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032718
98.7%
10000020
 
0.1%
5000018
 
0.1%
100013
 
< 0.1%
1000012
 
< 0.1%
2000012
 
< 0.1%
3000011
 
< 0.1%
500011
 
< 0.1%
50000010
 
< 0.1%
2000009
 
< 0.1%
Other values (192)313
 
0.9%
ValueCountFrequency (%)
032718
98.7%
501
 
< 0.1%
801
 
< 0.1%
1005
 
< 0.1%
3001
 
< 0.1%
3171
 
< 0.1%
4001
 
< 0.1%
5001
 
< 0.1%
5401
 
< 0.1%
6001
 
< 0.1%
ValueCountFrequency (%)
170000001
< 0.1%
158086741
< 0.1%
100200001
< 0.1%
70000001
< 0.1%
68891801
< 0.1%
60000002
< 0.1%
55000001
< 0.1%
50000001
< 0.1%
49250001
< 0.1%
41823821
< 0.1%

undisclosed
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct376
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90440.79449
Minimum0
Maximum250800000
Zeros32643
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:37.557827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum250800000
Range250800000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2171312.858
Coefficient of variation (CV)24.00811348
Kurtosis5931.492375
Mean90440.79449
Median Absolute Deviation (MAD)0
Skewness63.09075926
Sum2997841015
Variance4.714599525 × 1012
MonotonicityNot monotonic
2021-09-04T12:04:37.716101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032643
98.5%
100000012
 
< 0.1%
10000012
 
< 0.1%
50000011
 
< 0.1%
25000010
 
< 0.1%
200000010
 
< 0.1%
2000008
 
< 0.1%
3000007
 
< 0.1%
2650646
 
< 0.1%
2708626
 
< 0.1%
Other values (366)422
 
1.3%
ValueCountFrequency (%)
032643
98.5%
3441
 
< 0.1%
25001
 
< 0.1%
45001
 
< 0.1%
50001
 
< 0.1%
115661
 
< 0.1%
150002
 
< 0.1%
160421
 
< 0.1%
180001
 
< 0.1%
199001
 
< 0.1%
ValueCountFrequency (%)
2508000001
< 0.1%
1000000001
< 0.1%
910000001
< 0.1%
900000001
< 0.1%
873397701
< 0.1%
693374421
< 0.1%
650000002
< 0.1%
600000001
< 0.1%
570000001
< 0.1%
549012061
< 0.1%

convertible_note
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct248
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26872.93689
Minimum0
Maximum300000000
Zeros32707
Zeros (%)98.7%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:38.363171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum300000000
Range300000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1688646.036
Coefficient of variation (CV)62.83816479
Kurtosis30055.8765
Mean26872.93689
Median Absolute Deviation (MAD)0
Skewness169.7140714
Sum890757239
Variance2.851525436 × 1012
MonotonicityNot monotonic
2021-09-04T12:04:38.511043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032707
98.7%
10000016
 
< 0.1%
50000014
 
< 0.1%
5000013
 
< 0.1%
100000013
 
< 0.1%
15000012
 
< 0.1%
150000011
 
< 0.1%
20000010
 
< 0.1%
25000010
 
< 0.1%
20000008
 
< 0.1%
Other values (238)333
 
1.0%
ValueCountFrequency (%)
032707
98.7%
301
 
< 0.1%
1001
 
< 0.1%
5002
 
< 0.1%
10002
 
< 0.1%
20002
 
< 0.1%
50003
 
< 0.1%
52001
 
< 0.1%
60002
 
< 0.1%
100003
 
< 0.1%
ValueCountFrequency (%)
3000000001
< 0.1%
389794121
< 0.1%
200000001
< 0.1%
184724241
< 0.1%
162510911
< 0.1%
140250451
< 0.1%
137389761
< 0.1%
135201451
< 0.1%
100000001
< 0.1%
97228661
< 0.1%

debt_financing
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1465
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2197997.307
Minimum0
Maximum3.0079503 × 1010
Zeros29971
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:38.657181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1250000
Maximum3.0079503 × 1010
Range3.0079503 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation167186383.8
Coefficient of variation (CV)76.06305215
Kurtosis31607.64091
Mean2197997.307
Median Absolute Deviation (MAD)0
Skewness175.879695
Sum7.285701673 × 1010
Variance2.795128692 × 1016
MonotonicityNot monotonic
2021-09-04T12:04:38.807257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
029971
90.4%
100000083
 
0.3%
50000080
 
0.2%
300000072
 
0.2%
200000071
 
0.2%
10000069
 
0.2%
500000058
 
0.2%
20000049
 
0.1%
150000047
 
0.1%
25000045
 
0.1%
Other values (1455)2602
 
7.8%
ValueCountFrequency (%)
029971
90.4%
1001
 
< 0.1%
4001
 
< 0.1%
5001
 
< 0.1%
15001
 
< 0.1%
20001
 
< 0.1%
25003
 
< 0.1%
40001
 
< 0.1%
50008
 
< 0.1%
60001
 
< 0.1%
ValueCountFrequency (%)
3.0079503 × 10101
< 0.1%
24000000001
< 0.1%
22500000001
< 0.1%
12000000001
< 0.1%
7700000001
< 0.1%
7500000002
< 0.1%
7430000001
< 0.1%
7250000001
< 0.1%
7216443191
< 0.1%
6700000001
< 0.1%

angel
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct817
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74167.26132
Minimum0
Maximum30254390
Zeros30654
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:38.968366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile300000
Maximum30254390
Range30254390
Interquartile range (IQR)0

Descriptive statistics

Standard deviation558189.3333
Coefficient of variation (CV)7.526087972
Kurtosis871.3266251
Mean74167.26132
Median Absolute Deviation (MAD)0
Skewness23.44258673
Sum2458422211
Variance3.115753319 × 1011
MonotonicityNot monotonic
2021-09-04T12:04:39.123869image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030654
92.5%
500000154
 
0.5%
1000000154
 
0.5%
25000097
 
0.3%
10000094
 
0.3%
20000078
 
0.2%
30000073
 
0.2%
150000072
 
0.2%
200000063
 
0.2%
40000059
 
0.2%
Other values (807)1649
 
5.0%
ValueCountFrequency (%)
030654
92.5%
1201
 
< 0.1%
4391
 
< 0.1%
10001
 
< 0.1%
12001
 
< 0.1%
13501
 
< 0.1%
15001
 
< 0.1%
40001
 
< 0.1%
47001
 
< 0.1%
50004
 
< 0.1%
ValueCountFrequency (%)
302543901
 
< 0.1%
300000001
 
< 0.1%
207083161
 
< 0.1%
200000003
< 0.1%
180000001
 
< 0.1%
168374811
 
< 0.1%
150000002
< 0.1%
128040971
 
< 0.1%
125000001
 
< 0.1%
120000001
 
< 0.1%

grant
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct397
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145422.782
Minimum0
Maximum477475356
Zeros32413
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:39.275402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum477475356
Range477475356
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4965137.864
Coefficient of variation (CV)34.14277871
Kurtosis5667.760593
Mean145422.782
Median Absolute Deviation (MAD)0
Skewness69.77439829
Sum4820328954
Variance2.465259401 × 1013
MonotonicityNot monotonic
2021-09-04T12:04:39.420755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032413
97.8%
10000023
 
0.1%
15000021
 
0.1%
4000019
 
0.1%
100000019
 
0.1%
2500019
 
0.1%
50000019
 
0.1%
5000017
 
0.1%
300000016
 
< 0.1%
25000014
 
< 0.1%
Other values (387)567
 
1.7%
ValueCountFrequency (%)
032413
97.8%
3001
 
< 0.1%
10031
 
< 0.1%
20001
 
< 0.1%
30002
 
< 0.1%
40001
 
< 0.1%
50003
 
< 0.1%
65001
 
< 0.1%
70361
 
< 0.1%
80001
 
< 0.1%
ValueCountFrequency (%)
4774753561
< 0.1%
4120000001
< 0.1%
4000000001
< 0.1%
2518600001
< 0.1%
2060000001
< 0.1%
1910000001
< 0.1%
1325000001
< 0.1%
998000001
< 0.1%
980389201
< 0.1%
900000002
< 0.1%

private_equity
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct675
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2344468.343
Minimum0
Maximum3500000000
Zeros32135
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:39.551777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3500000000
Range3500000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation36374414.6
Coefficient of variation (CV)15.51499499
Kurtosis3716.049333
Mean2344468.343
Median Absolute Deviation (MAD)0
Skewness49.38797699
Sum7.771209216 × 1010
Variance1.323098037 × 1015
MonotonicityNot monotonic
2021-09-04T12:04:39.687465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032135
96.9%
10000000025
 
0.1%
500000024
 
0.1%
1000000015
 
< 0.1%
2000000012
 
< 0.1%
6000000011
 
< 0.1%
400000011
 
< 0.1%
2500000011
 
< 0.1%
200000010
 
< 0.1%
1500000010
 
< 0.1%
Other values (665)883
 
2.7%
ValueCountFrequency (%)
032135
96.9%
16601
 
< 0.1%
20001
 
< 0.1%
30641
 
< 0.1%
60001
 
< 0.1%
80001
 
< 0.1%
100001
 
< 0.1%
130001
 
< 0.1%
141681
 
< 0.1%
150001
 
< 0.1%
ValueCountFrequency (%)
35000000001
< 0.1%
26000000001
< 0.1%
17100000001
< 0.1%
14985153401
< 0.1%
11070000001
< 0.1%
10500000001
< 0.1%
9800000001
< 0.1%
7710000001
< 0.1%
7270000001
< 0.1%
7200400001
< 0.1%

post_ipo_equity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct138
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean607249.9052
Minimum0
Maximum4700000000
Zeros32982
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:39.830651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4700000000
Range4700000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30053778.04
Coefficient of variation (CV)49.49161422
Kurtosis18276.52816
Mean607249.9052
Median Absolute Deviation (MAD)0
Skewness122.7492992
Sum2.012851261 × 1010
Variance9.032295747 × 1014
MonotonicityNot monotonic
2021-09-04T12:04:39.972157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032982
99.5%
100000005
 
< 0.1%
200000005
 
< 0.1%
120000004
 
< 0.1%
300000003
 
< 0.1%
1000000003
 
< 0.1%
150000003
 
< 0.1%
13000002
 
< 0.1%
70000002
 
< 0.1%
170000002
 
< 0.1%
Other values (128)136
 
0.4%
ValueCountFrequency (%)
032982
99.5%
1192381
 
< 0.1%
1500001
 
< 0.1%
3000001
 
< 0.1%
3535001
 
< 0.1%
3750001
 
< 0.1%
6300001
 
< 0.1%
8000001
 
< 0.1%
10000002
 
< 0.1%
11000001
 
< 0.1%
ValueCountFrequency (%)
47000000001
< 0.1%
12000000001
< 0.1%
11000000001
< 0.1%
10000000002
< 0.1%
7392657761
< 0.1%
7360000001
< 0.1%
6454964641
< 0.1%
4860000001
< 0.1%
4780000001
< 0.1%
4277000001
< 0.1%

post_ipo_debt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243544.3775
Minimum0
Maximum2000000000
Zeros33104
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:40.105460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2000000000
Range2000000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16774235.44
Coefficient of variation (CV)68.8754781
Kurtosis11394.27075
Mean243544.3775
Median Absolute Deviation (MAD)0
Skewness102.1669249
Sum8072765480
Variance2.813749745 × 1014
MonotonicityNot monotonic
2021-09-04T12:04:40.257754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
033104
99.9%
50000003
 
< 0.1%
100000003
 
< 0.1%
350000002
 
< 0.1%
500000002
 
< 0.1%
250000002
 
< 0.1%
300000002
 
< 0.1%
1500000002
 
< 0.1%
200000002
 
< 0.1%
2390000001
 
< 0.1%
Other values (24)24
 
0.1%
ValueCountFrequency (%)
033104
99.9%
907501
 
< 0.1%
5900001
 
< 0.1%
8000001
 
< 0.1%
11000001
 
< 0.1%
15000001
 
< 0.1%
20000001
 
< 0.1%
50000003
 
< 0.1%
75000001
 
< 0.1%
100000003
 
< 0.1%
ValueCountFrequency (%)
20000000001
< 0.1%
19000000001
< 0.1%
9200000001
< 0.1%
6000000001
< 0.1%
3900000001
< 0.1%
3000000001
< 0.1%
2390000001
< 0.1%
2223000001
< 0.1%
2103847301
< 0.1%
1750000001
< 0.1%

secondary_market
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34282.80028
Minimum0
Maximum400000000
Zeros33132
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:40.394794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum400000000
Range400000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2848075.518
Coefficient of variation (CV)83.07593004
Kurtosis13489.62106
Mean34282.80028
Median Absolute Deviation (MAD)0
Skewness109.3906662
Sum1136371981
Variance8.111534156 × 1012
MonotonicityNot monotonic
2021-09-04T12:04:40.507923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
033132
> 99.9%
73437891
 
< 0.1%
2100000001
 
< 0.1%
1267000001
 
< 0.1%
61923961
 
< 0.1%
637500001
 
< 0.1%
1569291
 
< 0.1%
600000001
 
< 0.1%
2000000001
 
< 0.1%
125000001
 
< 0.1%
Other values (6)6
 
< 0.1%
ValueCountFrequency (%)
033132
> 99.9%
1569291
 
< 0.1%
2100001
 
< 0.1%
25000001
 
< 0.1%
61923961
 
< 0.1%
73437891
 
< 0.1%
77188671
 
< 0.1%
125000001
 
< 0.1%
193000001
 
< 0.1%
200000001
 
< 0.1%
ValueCountFrequency (%)
4000000001
< 0.1%
2100000001
< 0.1%
2000000001
< 0.1%
1267000001
< 0.1%
637500001
< 0.1%
600000001
< 0.1%
200000001
< 0.1%
193000001
< 0.1%
125000001
< 0.1%
77188671
< 0.1%

product_crowdfunding
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct126
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8621.171237
Minimum0
Maximum72000000
Zeros32999
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:40.647453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72000000
Range72000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation513363.5954
Coefficient of variation (CV)59.54685057
Kurtosis14880.70206
Mean8621.171237
Median Absolute Deviation (MAD)0
Skewness116.33056
Sum285765963
Variance2.635421811 × 1011
MonotonicityNot monotonic
2021-09-04T12:04:40.798138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032999
99.6%
5000004
 
< 0.1%
1000004
 
< 0.1%
100004
 
< 0.1%
10000004
 
< 0.1%
250002
 
< 0.1%
3788122
 
< 0.1%
16725802
 
< 0.1%
15152512
 
< 0.1%
1500002
 
< 0.1%
Other values (116)122
 
0.4%
ValueCountFrequency (%)
032999
99.6%
10001
 
< 0.1%
100004
 
< 0.1%
120001
 
< 0.1%
153881
 
< 0.1%
160001
 
< 0.1%
204461
 
< 0.1%
250002
 
< 0.1%
300001
 
< 0.1%
340331
 
< 0.1%
ValueCountFrequency (%)
720000001
< 0.1%
520000001
< 0.1%
146000001
< 0.1%
110083761
< 0.1%
103000001
< 0.1%
100000001
< 0.1%
86000001
< 0.1%
62253541
< 0.1%
55000001
< 0.1%
45457542
< 0.1%

round_A
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1504
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1436409.012
Minimum0
Maximum225000000
Zeros26195
Zeros (%)79.0%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:40.940873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8000000
Maximum225000000
Range225000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5550443.408
Coefficient of variation (CV)3.864110683
Kurtosis371.3555593
Mean1436409.012
Median Absolute Deviation (MAD)0
Skewness14.17591341
Sum4.761264952 × 1010
Variance3.080742203 × 1013
MonotonicityNot monotonic
2021-09-04T12:04:41.073360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
026195
79.0%
5000000413
 
1.2%
2000000296
 
0.9%
3000000294
 
0.9%
4000000260
 
0.8%
6000000251
 
0.8%
10000000235
 
0.7%
1000000215
 
0.6%
1500000177
 
0.5%
7000000167
 
0.5%
Other values (1494)4644
 
14.0%
ValueCountFrequency (%)
026195
79.0%
41
 
< 0.1%
2911
 
< 0.1%
7151
 
< 0.1%
17001
 
< 0.1%
20001
 
< 0.1%
65001
 
< 0.1%
70001
 
< 0.1%
129891
 
< 0.1%
145421
 
< 0.1%
ValueCountFrequency (%)
2250000002
< 0.1%
2000000001
< 0.1%
1760000001
< 0.1%
1706040001
< 0.1%
1650000001
< 0.1%
1500000001
< 0.1%
1300000001
< 0.1%
1100000001
< 0.1%
1080000001
< 0.1%
1040000001
< 0.1%

round_B
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1031
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1860555.944
Minimum0
Maximum542000000
Zeros28688
Zeros (%)86.5%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:41.215951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12000000
Maximum542000000
Range542000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8545641.72
Coefficient of variation (CV)4.593058193
Kurtosis800.245533
Mean1860555.944
Median Absolute Deviation (MAD)0
Skewness19.56464728
Sum6.167184787 × 1010
Variance7.302799241 × 1013
MonotonicityNot monotonic
2021-09-04T12:04:41.360650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
028688
86.5%
10000000325
 
1.0%
15000000177
 
0.5%
8000000176
 
0.5%
5000000153
 
0.5%
12000000145
 
0.4%
20000000140
 
0.4%
6000000134
 
0.4%
7000000120
 
0.4%
400000096
 
0.3%
Other values (1021)2993
 
9.0%
ValueCountFrequency (%)
028688
86.5%
10001
 
< 0.1%
20001
 
< 0.1%
26661
 
< 0.1%
40001
 
< 0.1%
299831
 
< 0.1%
500002
 
< 0.1%
888881
 
< 0.1%
1000003
 
< 0.1%
1585791
 
< 0.1%
ValueCountFrequency (%)
5420000001
< 0.1%
3551870001
< 0.1%
3500000001
< 0.1%
3200000001
< 0.1%
2500000001
< 0.1%
2000000001
< 0.1%
1850000001
< 0.1%
1650000001
< 0.1%
1600000001
< 0.1%
1570000001
< 0.1%

round_C
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct618
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1574122.651
Minimum0
Maximum490000000
Zeros30727
Zeros (%)92.7%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:41.517847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10000000
Maximum490000000
Range490000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9299864.428
Coefficient of variation (CV)5.907966844
Kurtosis553.4517167
Mean1574122.651
Median Absolute Deviation (MAD)0
Skewness17.24578542
Sum5.21774435 × 1010
Variance8.648747838 × 1013
MonotonicityNot monotonic
2021-09-04T12:04:41.659460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030727
92.7%
10000000143
 
0.4%
15000000138
 
0.4%
20000000115
 
0.3%
2500000089
 
0.3%
1200000073
 
0.2%
3000000069
 
0.2%
500000057
 
0.2%
700000046
 
0.1%
800000046
 
0.1%
Other values (608)1644
 
5.0%
ValueCountFrequency (%)
030727
92.7%
20001
 
< 0.1%
762651
 
< 0.1%
1000001
 
< 0.1%
1500001
 
< 0.1%
1999991
 
< 0.1%
2000001
 
< 0.1%
2727451
 
< 0.1%
3000001
 
< 0.1%
3037571
 
< 0.1%
ValueCountFrequency (%)
4900000001
 
< 0.1%
3750000001
 
< 0.1%
3500000002
< 0.1%
3000000001
 
< 0.1%
2580000001
 
< 0.1%
2160000001
 
< 0.1%
2000000003
< 0.1%
1987306771
 
< 0.1%
1940000001
 
< 0.1%
1600000002
< 0.1%

round_D
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct398
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1010365.573
Minimum0
Maximum1200000000
Zeros32024
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:41.813007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1200000000
Range1200000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11800671.52
Coefficient of variation (CV)11.67960571
Kurtosis4667.165367
Mean1010365.573
Median Absolute Deviation (MAD)0
Skewness54.86374714
Sum3.349058766 × 1010
Variance1.392558482 × 1014
MonotonicityNot monotonic
2021-09-04T12:04:41.966877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032024
96.6%
2000000060
 
0.2%
1000000058
 
0.2%
1500000055
 
0.2%
2500000040
 
0.1%
1200000039
 
0.1%
5000000032
 
0.1%
3000000030
 
0.1%
4000000024
 
0.1%
800000020
 
0.1%
Other values (388)765
 
2.3%
ValueCountFrequency (%)
032024
96.6%
50001
 
< 0.1%
850001
 
< 0.1%
1000001
 
< 0.1%
1242331
 
< 0.1%
1500001
 
< 0.1%
2692281
 
< 0.1%
4000001
 
< 0.1%
5000001
 
< 0.1%
10000002
 
< 0.1%
ValueCountFrequency (%)
12000000001
 
< 0.1%
9500000001
 
< 0.1%
4750000001
 
< 0.1%
4200000001
 
< 0.1%
3000000001
 
< 0.1%
2800000001
 
< 0.1%
2500000002
< 0.1%
2200000001
 
< 0.1%
2100000001
 
< 0.1%
2000000003
< 0.1%

round_E
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct206
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean471264.8938
Minimum0
Maximum400000000
Zeros32686
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:42.117454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum400000000
Range400000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6445724.534
Coefficient of variation (CV)13.67749777
Kurtosis1167.502976
Mean471264.8938
Median Absolute Deviation (MAD)0
Skewness28.35215798
Sum1.562101743 × 1010
Variance4.154736477 × 1013
MonotonicityNot monotonic
2021-09-04T12:04:42.253955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032686
98.6%
2000000023
 
0.1%
2500000019
 
0.1%
1000000017
 
0.1%
3000000017
 
0.1%
4000000014
 
< 0.1%
1500000014
 
< 0.1%
5000000013
 
< 0.1%
800000011
 
< 0.1%
1200000010
 
< 0.1%
Other values (196)323
 
1.0%
ValueCountFrequency (%)
032686
98.6%
3270541
 
< 0.1%
4875001
 
< 0.1%
5399991
 
< 0.1%
9800001
 
< 0.1%
12000001
 
< 0.1%
18679731
 
< 0.1%
20000002
 
< 0.1%
20817721
 
< 0.1%
24000001
 
< 0.1%
ValueCountFrequency (%)
4000000001
< 0.1%
3600000001
< 0.1%
2500000002
< 0.1%
2270000001
< 0.1%
2250000001
< 0.1%
2200000001
< 0.1%
2000000001
< 0.1%
1754453821
< 0.1%
1700000001
< 0.1%
1677000001
< 0.1%

round_F
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct101
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242787.3802
Minimum0
Maximum1060000000
Zeros32991
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:42.426368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1060000000
Range1060000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7642606.186
Coefficient of variation (CV)31.47859737
Kurtosis11423.50336
Mean242787.3802
Median Absolute Deviation (MAD)0
Skewness90.23753602
Sum8047673291
Variance5.840942932 × 1013
MonotonicityNot monotonic
2021-09-04T12:04:42.580709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032991
99.5%
2000000010
 
< 0.1%
500000008
 
< 0.1%
400000007
 
< 0.1%
150000005
 
< 0.1%
250000005
 
< 0.1%
100000004
 
< 0.1%
350000004
 
< 0.1%
2000000003
 
< 0.1%
70000003
 
< 0.1%
Other values (91)107
 
0.3%
ValueCountFrequency (%)
032991
99.5%
2715001
 
< 0.1%
6583621
 
< 0.1%
8000001
 
< 0.1%
10000001
 
< 0.1%
11600001
 
< 0.1%
19999991
 
< 0.1%
20000001
 
< 0.1%
24251011
 
< 0.1%
26800001
 
< 0.1%
ValueCountFrequency (%)
10600000001
 
< 0.1%
2860000001
 
< 0.1%
2500000001
 
< 0.1%
2491011591
 
< 0.1%
2300000001
 
< 0.1%
2250000001
 
< 0.1%
2100000001
 
< 0.1%
2000000003
< 0.1%
1760000001
 
< 0.1%
1500000002
< 0.1%

round_G
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83682.27788
Minimum0
Maximum1000000000
Zeros33115
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:42.712976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1000000000
Range1000000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6401507.478
Coefficient of variation (CV)76.49776799
Kurtosis18709.33447
Mean83682.27788
Median Absolute Deviation (MAD)0
Skewness128.2336611
Sum2773816465
Variance4.097929799 × 1013
MonotonicityNot monotonic
2021-09-04T12:04:42.840865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
033115
99.9%
100000003
 
< 0.1%
250000002
 
< 0.1%
560000001
 
< 0.1%
1000000001
 
< 0.1%
4000000001
 
< 0.1%
990000001
 
< 0.1%
150000001
 
< 0.1%
69379191
 
< 0.1%
260000001
 
< 0.1%
Other values (20)20
 
0.1%
ValueCountFrequency (%)
033115
99.9%
8184271
 
< 0.1%
17000001
 
< 0.1%
37000001
 
< 0.1%
68000001
 
< 0.1%
69379191
 
< 0.1%
81999991
 
< 0.1%
100000003
 
< 0.1%
125000001
 
< 0.1%
150000001
 
< 0.1%
ValueCountFrequency (%)
10000000001
< 0.1%
4000000001
< 0.1%
3500000001
< 0.1%
1720000001
< 0.1%
1000000001
< 0.1%
990000001
< 0.1%
699854351
< 0.1%
630000001
< 0.1%
600000001
< 0.1%
560000001
< 0.1%

round_H
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size259.1 KiB
0.0
33143 
50000000.0
 
1
600000000.0
 
1
49000000.0
 
1
4600000.0
 
1

Length

Max length11
Median length3
Mean length3.000844722
Min length3

Characters and Unicode

Total characters99469
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.033143
> 99.9%
50000000.01
 
< 0.1%
600000000.01
 
< 0.1%
49000000.01
 
< 0.1%
4600000.01
 
< 0.1%

Length

2021-09-04T12:04:43.092024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-04T12:04:43.181251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.033143
> 99.9%
50000000.01
 
< 0.1%
600000000.01
 
< 0.1%
49000000.01
 
< 0.1%
4600000.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
066316
66.7%
.33147
33.3%
62
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number66322
66.7%
Other Punctuation33147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
066316
> 99.9%
62
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
91
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.33147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common99469
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
066316
66.7%
.33147
33.3%
62
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII99469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
066316
66.7%
.33147
33.3%
62
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
91
 
< 0.1%

total_disclosed_venture_funding
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct9666
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16077940.63
Minimum0
Maximum4702140000
Zeros7657
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size259.1 KiB
2021-09-04T12:04:43.294391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121189
median1000000
Q310000000
95-th percentile78000000
Maximum4702140000
Range4702140000
Interquartile range (IQR)9978811

Descriptive statistics

Standard deviation63483643.09
Coefficient of variation (CV)3.948493438
Kurtosis1275.806407
Mean16077940.63
Median Absolute Deviation (MAD)1000000
Skewness25.33427115
Sum5.329354982 × 1011
Variance4.03017294 × 1015
MonotonicityNot monotonic
2021-09-04T12:04:43.453446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07657
 
23.1%
1000000489
 
1.5%
500000457
 
1.4%
100000427
 
1.3%
2000000395
 
1.2%
40000363
 
1.1%
250000307
 
0.9%
50000281
 
0.8%
200000260
 
0.8%
10000000259
 
0.8%
Other values (9656)22252
67.1%
ValueCountFrequency (%)
07657
23.1%
601
 
< 0.1%
5821
 
< 0.1%
100015
 
< 0.1%
12001
 
< 0.1%
12651
 
< 0.1%
13051
 
< 0.1%
13501
 
< 0.1%
15004
 
< 0.1%
15061
 
< 0.1%
ValueCountFrequency (%)
47021400001
< 0.1%
30134500001
< 0.1%
24020000001
< 0.1%
22568000001
< 0.1%
17267637861
< 0.1%
16262250391
< 0.1%
15890200001
< 0.1%
15500000001
< 0.1%
15245000001
< 0.1%
15203330221
< 0.1%

Cluster_NO_PCA
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size259.1 KiB
0
31694 
3
 
1402
1
 
47
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33147
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

Length

2021-09-04T12:04:43.686678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-04T12:04:43.769327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33147
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common33147
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII33147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

kmean_NO_PCA
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size259.1 KiB
0
31694 
3
 
1402
1
 
47
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33147
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

Length

2021-09-04T12:04:43.982898image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-04T12:04:44.061844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33147
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common33147
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII33147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
031694
95.6%
31402
 
4.2%
147
 
0.1%
24
 
< 0.1%

ClusterWITH_PCA
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size259.1 KiB
1
31686 
0
 
1410
2
 
47
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33147
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

Length

2021-09-04T12:04:44.264424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-04T12:04:44.335565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33147
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common33147
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII33147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

kmean_WITH_PCA
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size259.1 KiB
1
31686 
0
 
1410
2
 
47
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33147
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

Length

2021-09-04T12:04:44.525069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-04T12:04:44.595049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33147
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common33147
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII33147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131686
95.6%
01410
 
4.3%
247
 
0.1%
34
 
< 0.1%

Interactions

2021-09-04T12:03:01.254290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:01.451972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:01.569195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:01.696071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:01.811030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:01.942668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:02.074478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:02.227460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:02.354725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:02.498369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:02.632681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:02.764136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:02.889120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:03.050766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:03.446998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:03.581889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:03.741766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:03.875943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:04.006584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:04.154526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:04.357982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:04.506510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:04.628614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:04.756793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:04.889216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:05.018630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:05.139677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:05.296118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:05.437552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:05.566370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:05.698787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:05.852941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:06.023640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:06.218723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:06.395559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:06.550647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:06.698554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:06.846258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:06.976052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:07.116195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:07.255834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:07.423895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:07.860563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:08.074691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:08.516669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:08.770730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:08.931109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:09.052544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:09.172609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:09.308996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:09.440512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:09.593282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:09.720179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:09.865231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:09.997802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:10.130325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:10.252264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:10.390334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:10.523608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:10.747395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:10.899027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:11.040978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:11.182233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:11.378496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:11.518501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:11.697853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:11.908557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:12.058370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:12.252840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:12.408565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:12.549307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:12.686732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:12.816979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:12.949408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:13.089025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:13.223251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:13.356833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:13.496844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:13.789782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:14.042731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:14.186313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:14.315852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:14.621998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:14.841728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:15.013090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:15.163340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:15.317972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:15.493147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:15.692455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:15.865936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:16.013669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:16.149387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:16.270030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:16.475077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:16.694881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:16.876426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:17.018510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:17.177199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:17.397694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:17.727887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:17.935577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:18.108169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:18.277436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:18.454320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:18.614233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:18.800128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:19.028622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:19.236293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:19.446136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:19.650860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:19.845835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:20.034482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:20.253146image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:20.589361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:20.901579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:21.176462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:21.461565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:21.734652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:22.031573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:22.368264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:22.633725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:22.870538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:23.128571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:23.379807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:23.627597image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:23.881770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:24.133179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:03:24.610407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-09-04T12:04:32.974683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:04:33.093911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-04T12:04:33.216428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-04T12:04:44.745590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-04T12:04:45.112844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-04T12:04:45.481970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-04T12:04:45.837441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-04T12:04:46.234086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-04T12:04:33.572566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-04T12:04:34.594824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

namemarketstatusfunding_roundsseedventureequity_crowdfundingundisclosedconvertible_notedebt_financingangelgrantprivate_equitypost_ipo_equitypost_ipo_debtsecondary_marketproduct_crowdfundinground_Around_Bround_Cround_Dround_Eround_Fround_Ground_Htotal_disclosed_venture_fundingCluster_NO_PCAkmean_NO_PCAClusterWITH_PCAkmean_WITH_PCA
0#waywire001.01750000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01750000.00011
1'Rock' Your Paper111.040000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.040000.00011
2(In)Touch Network211.01500000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01500000.00011
3-R- Ranch and Mine312.00.00.060000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00011
4004 Technologies411.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00011
51,2,3 Listo511.040000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.040000.00011
61-800-DENTIST611.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00011
71-800-DOCTORS611.00.00.00.00.01750000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00011
81.618 Technology711.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00011
910 Minutes With812.0400000.04000000.00.00.00.00.00.00.00.00.00.00.00.04000000.00.00.00.00.00.00.00.08400000.00011

Last rows

namemarketstatusfunding_roundsseedventureequity_crowdfundingundisclosedconvertible_notedebt_financingangelgrantprivate_equitypost_ipo_equitypost_ipo_debtsecondary_marketproduct_crowdfundinground_Around_Bround_Cround_Dround_Eround_Fround_Ground_Htotal_disclosed_venture_fundingCluster_NO_PCAkmean_NO_PCAClusterWITH_PCAkmean_WITH_PCA
33137Zynerba Pharmaceuticals2111.00.013000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.300000e+070011
33138Zynga8919.00.0866550786.00.00.00.00.00.00.00.00.00.00.00.015026000.0355187000.0490000000.00.00.00.00.00.01.726764e+091122
33139Zyngenia1711.00.025000000.00.00.00.00.00.00.00.00.00.00.00.025000000.00.00.00.00.00.00.00.05.000000e+070011
33140Zynstra414.0225000.012200000.00.00.00.00.02325000.00.00.00.00.00.00.03800000.08400000.00.00.00.00.00.00.02.695000e+070011
33141ZYOMYX1714.00.026775015.00.00.00.00.00.07500000.00.00.00.00.00.00.012000000.00.00.00.00.00.00.03.877502e+070011
33142Zyraz Technology1724.00.07991547.00.00.00.00.02007363.05400000.020967.00.00.00.00.07991547.00.00.00.00.00.00.00.01.799046e+070011
33143Zytoprotec1711.00.02686600.00.00.00.00.00.00.00.00.00.00.00.02686600.00.00.00.00.00.00.00.05.373200e+060011
33144Zzish811.0320000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03.200000e+050011
33145Zzzzapp Wireless ltd.5615.071525.00.00.00.025873.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.07.152500e+040011
33146[x+1]3614.00.028000000.00.00.00.017000000.00.00.00.00.00.00.00.016000000.010000000.00.00.00.00.00.00.05.400000e+070011